@article {5086, title = {Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx}, journal = {Frontiers in Pharmacology}, volume = {12}, year = {2022}, month = {01/2022}, abstract = {

Clinical populations have memory deficits linked to sleep oscillations that can potentially be treated with sleep medications. Eszopiclone and zolpidem (two non-benzodiazepine hypnotics) both enhance sleep spindles. Zolpidem improved sleep-dependent memory consolidation in humans, but eszopiclone did not. These divergent results may reflect that the two drugs have different effects on hippocampal ripple oscillations, which correspond to the reactivation of neuronal ensembles that represent previous waking activity and contribute to memory consolidation. We used extracellular recordings in the CA1 region of rats and systemic dosing of eszopiclone and zolpidem to test the hypothesis that these two drugs differentially affect hippocampal ripples and spike activity. We report evidence that eszopiclone makes ripples sparser, while zolpidem increases ripple density. In addition, eszopiclone led to a drastic decrease in spike firing, both in putative pyramidal cells and interneurons, while zolpidem did not substantially alter spiking. These results provide an explanation of the different effects of eszopiclone and zolpidem on memory in human studies and suggest that sleep medications can be used to regulate hippocampal ripple oscillations, which are causally linked to sleep-dependent memory consolidation.

}, doi = {10.3389/fphar.2021.792148}, url = {https://www.frontiersin.org/articles/10.3389/fphar.2021.792148/full}, author = {Becker, Logan A. and Hector Penagos and Francisco J. Flores and Manoach, Dara S. and Matthew A. Wilson and Varela, Carmen} } @article {5083, title = {Temporally delayed linear modelling (TDLM) measures replay in both animals and humans}, journal = {eLife}, volume = {10}, year = {2021}, month = {06/2021}, abstract = {

There are rich structures in off-task neural activity which are hypothesized to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit - temporal delayed linear modelling (TDLM) - for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, for example, its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.

}, doi = {10.7554/eLife.66917}, url = {https://elifesciences.org/articles/66917}, author = {Liu, Yunzhe and Dolan, Raymond J and Higgins, Cameron and Hector Penagos and Woolrich, Mark W and {\'O}lafsd{\'o}ttir, H Freyja and Barry, Caswell and Kurth-Nelson, Zeb and Behrens, Timothy E} } @conference {4522, title = {Disruption of CA1 Sharp-Wave Ripples by the nonbenzodiazepine hypnotic eszopiclone }, booktitle = {Society for Neuroscience}, year = {2019}, month = {10/2019}, address = {Chicago, IL, USA}, author = {Becker, LA and Hector Penagos and Manoach, DS and Matthew A. Wilson and Varela, Carmen} } @article {3990, title = {Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes}, journal = {Cell Reports}, volume = {25}, year = {2018}, month = {Jan-12-2018}, pages = {2635 - 2642.e5}, abstract = {

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents{\textquoteright} unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an \~{}20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time \<15 ms), our approach enables assessment of the statistical significance of online-decoded {\textquotedblleft}memory replay{\textquotedblright} candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.

}, keywords = {GPU, memory replay, neural decoding, place codes, population decoding, spatiotemporal patterns}, issn = {22111247}, doi = {https://doi.org/10.1016/j.celrep.2018.11.033}, url = {https://www.sciencedirect.com/science/article/pii/S2211124718317960}, author = {Hu, Sile and Ciliberti, Davide and Grosmark, Andres D. and Michon, {\'e}d{\'e}ric and Ji, Daoyun and Hector Penagos and {\'a}ki, {\"o}rgy and Matthew A. Wilson and Kloosterman, Fabian and Chen, Zhe} } @article {2943, title = {Oscillations, neural computations and learning during wake and sleep}, journal = {Current Opinion in Neurobiology}, volume = {44C}, year = {2017}, month = {07/2017}, type = {Review}, chapter = {193}, doi = {https://doi.org/10.1016/j.conb.2017.05.009}, author = {Hector Penagos and Varela, Carmen and Matthew A. Wilson} } @article {2194, title = {Uncovering representations of sleep-associated hippocampal ensemble spike activity}, journal = {Scientific Reports}, volume = {6}, year = {2016}, month = {08/2016}, abstract = {

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

}, doi = {10.1038/srep32193}, url = {http://dx.doi.org/10.1038/srep32193}, author = {Zhe Chen and Andres D. Grosmark and Hector Penagos and Matthew A. Wilson} }